ECG measuring devices are primarily used to measure and monitor the cardiac function of a patient, for which purpose the total voltage of the electrical activity of the myocardial fibers is typically measured as what is known as an “ECG signal” by way of at least two electrodes.
There are however further applications. For example ECG signals are also used in medical imaging to generate trigger signals. During imaging the ECG signal is used to obtain information about the cardiac phase, in order thus to synchronize imaging with cardiac activity. In particular with imaging methods which require a fairly long recording time it is thus possible to produce high-quality cardiac recordings as well as recordings of regions moved by the heartbeat.
ECG measuring devices for the in-situ recording of ECG signals are also used while a patient is being examined using a magnetic resonance device. However operation in the magnetic resonance device, because of the strong gradient fields and high-frequency fields used therein for imaging purposes, means that the ECG measuring device has to meet particular requirements in order to prevent mutual interference between the magnetic resonance device and the ECG measuring device. ECG measuring devices, which are magnetic resonance-compatible in the above sense, are available on the market.
As always a major problem for reliable ECG signal measurement is magnetic fields, which change over time, as used in the magnetic resonance device as magnetic gradient fields for local coding. According to the law of induction such magnetic fields, which change over time, generate interference voltages, which are injected as interference in the ECG signal recorded by the ECG electrodes. Such magnetically generated interference signals are superimposed on the ECG signal generated by the heart and falsify it.
A signal data record U1(t) measured at a first channel of the ECG measuring device then contains not only the desired ECG signal U1 ECG(t) at time t, but a superposition of the ECG signal and the interference voltages S1(t) generated by induction at time t:U1(t)=U1 ECG(t)+S1(t).
This interference is extremely undesirable. Synchronization of a recording of a magnetic resonance image with the heartbeat requires reliable identification of the R-wave of the ECG signal. The interference signals can be interpreted erroneously as an R-wave, for example because of their often similar form, and can therefore wrongly initiate the triggering of a recording of a magnetic resonance image. On the other hand it can also happen that a “true” R-wave is not identified as such because of the superimposed interference signals. This regularly causes a significant deterioration in image quality.
Known from the publications “Restoration of Electrophysiological Signals Distorted by Inductive Effects of Magnetic Field Gradients During MR Sequences”; Jacques Felblinger, Johannes Slotboom, Roland Kreis, Bruno Jung, Chris Boesch; Magnetic Resonance in Medicine 41:715-721 (1999), and “Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction during MRI Data Acquisition”; Freddy Odille, Cedric Pasquier, Roger Abächerli, Pierre-Andre Vuissoz, Gary P. Zientara, Jacques Felblinger; IEEE Transactions on Biomedical Engineering, VOL. 54, NO. 4, APRIL 2007, is a method, in which the interference injections caused by the gradient fields and therefore the interference voltages are estimated. The estimated interference voltage of an ECG channel S1(t) is then subtracted from the ECG signals U1(t) measured at the same ECG channel, to obtain a corrected ECG signal U1 corr(t):U1 corr(t)=U1 ECG(t)+S1(t)−S1 est(t).
It is assumed here that the interference voltages S1(t) can be separated into interference voltages S1x(t), S1y(t) and S1z(t), caused respectively by the known currents Ix(t), Iy(t) and Iz(t) applied to the x, y and z axis gradient coils:
                              S          ⁢                                          ⁢          1          ⁢                      (            t            )                          =                ⁢                              S            ⁢                                                  ⁢            1            ⁢                          x              ⁡                              (                t                )                                              +                      S            ⁢                                                  ⁢            1            ⁢                          y              ⁡                              (                t                )                                              +                      S            ⁢                                                  ⁢            1            ⁢                          z              ⁡                              (                t                )                                                                            =                ⁢                              hIxU            ⁢                                                  ⁢            1            ⁢                          (              t              )                        *                          Ix              ⁡                              (                t                )                                              +                      hIyU            ⁢                                                  ⁢            1            ⁢                          (              t              )                        *                          Iy              ⁡                              (                t                )                                              +                      hIzU            ⁢                                                  ⁢            1            ⁢                          (              t              )                        *                                          Iz                ⁡                                  (                  t                  )                                            .                                          
Here h Ii U1(t) (i=x,y,z) is the respective pulse response, which characterizes the influence of the current Ii(t) through the i-axis gradient coil on the ECG signal U1(t). “*” characterizes a systems theory convolution. The axes x, y and z are perpendicular to one another here, with the x-axis typically corresponding to a normal vector to a sagittal plane, the y-axis a normal vector to a coronary plane and the z-axis a normal Vector to a transverse plane through a patient in a magnetic resonance device.
The above-mentioned pulse responses h Ii U1(t) are estimated in that ECG signals U1(t) are measured in training measurements for example, when a current Ii(t) not equal to zero is applied respectively to just one of the gradient coils, so that the following applies for i=x for example:U1(t)=U1 ECG(t)+h Ix U1(t)*Ix(t).
It is possible to estimate the pulse response h Ix U1(t) from this equation means of calculations in the frequency range. In this process the contribution of U1 ECG(t) can be calculated out for example by multiple measurement of U1(t) and subsequent averaging. The procedure is similar for the further pulse responses. The following thus results:S1 est(t)=h Ix U1 est(t)*Ix(t)+h Iy U1 est(t)*Iy(t)+h Iz U1 est(t)*Iz(t).
Further data can be found in the above-mentioned prior art.
Good results are achieved with this method, if ECG signals measured under the same conditions, which also prevailed during the above-mentioned training measurements, are corrected. If these conditions change, for example if the patient changes position due to the respiration of said patient thereby causing the position of the ECG measuring device in the magnetic resonance device to change as well, the result of the interference estimate deteriorates, so that residual interference cannot be avoided and an ECG signal cannot be corrected optimally.